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Jie Tang

23 papers hereh-index 206k citations34 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author16

Across the 21 of 23 papers where every author was matched, so the position is known.

fields
  • cs.LG12
  • cs.CL5
  • cs.CV2
  • cs.SI2
  • cs.AI1
  • q-bio.QM1
same name
  • Jie Tang — 54 papers, h 82
  • Jie Tang — 32 papers, h 31
  • Jie Tang — 23 papers, h 21
  • Jie Tang — 23 papers, h 16
  • Jie Tang — 21 papers, h 13
  • Jie Tang — 19 papers, h 52

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20212024
most citedGLM-130B: An Open Bilingual Pre-trained Model

298 citations · 716 across the 23 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.SI2021★ 1 cited

SCR: Training Graph Neural Networks with Consistency Regularization

Chenhui Zhang, Yufei He, Yukuo Cen +7

We present the SCR framework for enhancing the training of graph neural networks (GNNs) with consistency regularization. Regularization is a set of strategies used in Machine Learn…

cs.LG2021★ 1 cited

Graph Contrastive Learning for Anomaly Detection

Bo Chen, Jing Zhang, Xiaokang Zhang +6

Graph-based anomaly detection has been widely used for detecting malicious activities in real-world applications. Existing attempts to address this problem have thus far focused on…

cs.CL2021★ 27 cited

OAG-BERT: Towards A Unified Backbone Language Model For Academic Knowledge Services

Xiao Liu, Da Yin, Jingnan Zheng +5

Academic knowledge services have substantially facilitated the development of the science enterprise by providing a plenitude of efficient research tools. However, many application…

cs.SI2021★ 5 cited

CogDL: A Comprehensive Library for Graph Deep Learning

Yukuo Cen, Zhenyu Hou, Yan Wang +15

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning community in recent years. It has been widely adopted in various real-world applications fr…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.